使用独立成分分析对脑动力学进行成像
Imaging Brain Dynamics Using Independent Component Analysis.
作者信息
Jung Tzyy-Ping, Makeig Scott, McKeown Martin J, Bell Anthony J, Lee Te-Won, Sejnowski Terrence J
机构信息
University of California at San Diego, La Jolla, CA 92093-0523 USA and also with The Salk Institute for Biological Studies, La Jolla, CA 92037 USA.
出版信息
Proc IEEE Inst Electr Electron Eng. 2001 Jul 1;89(7):1107-1122. doi: 10.1109/5.939827.
Abstract
The analysis of electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings is important both for basic brain research and for medical diagnosis and treatment. Independent component analysis (ICA) is an effective method for removing artifacts and separating sources of the brain signals from these recordings. A similar approach is proving useful for analyzing functional magnetic resonance brain imaging (fMRI) data. In this paper, we outline the assumptions underlying ICA and demonstrate its application to a variety of electrical and hemodynamic recordings from the human brain.
摘要
脑电图（EEG）和脑磁图（MEG）记录分析对于基础脑研究以及医学诊断和治疗均具有重要意义。独立成分分析（ICA）是一种从这些记录中去除伪迹并分离脑信号源的有效方法。一种类似的方法已被证明对分析功能磁共振脑成像（fMRI）数据很有用。在本文中，我们概述了ICA所基于的假设，并展示了其在来自人脑的各种电生理和血液动力学记录中的应用。
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